1. Field of the Invention
This invention relates to methods, processors and computed tomography (CT) machines for generating images utilizing high and low sensitivity data collected from a flat panel detector having an extended dynamic range.
2. Background Art
Referring to FIGS. 1 and 2, flat panel imagers (also referred to as flat panel arrays or flat panel detectors) (i.e., FPIs) are large-area arrays with pixels consisting of a hydrogenated amorphous silicon (a-Si:H) thin-film transistor 1 (TFT) coupled to an a-Si:H photodiode sensor 2, as shown in U.S. Pat. No. 5,262,649.
On top of the array is an X-ray converter 3 based on a phosphor material such as Gd2O2S:Tb or scintillating material such as CsI:(T1). When an X-ray photon reaches the X-ray converter 3 (also referred to as a screen), it stimulates the emission of light photons which are then detected by the reverse-biased photodiode sensor 2. The reverse bias, together with a large intrinsic region of the diode, creates a capacitor in which the light photons generate electron hole pairs that effectively discharge the capacitor. The amount of discharge constitutes the information. During this phase, the TFT 1 is in the “off”, or non-conducting, position which is achieved by setting its control line 4 (also referred to as gate line) to a negative voltage (e.g., −5V). In the next phase—readout phase—an entire row of TFTs is set to the “on” or conducting position by setting their gate line 4 to +5V, allowing the charge to flow out through all DATA lines 5 simultaneously, while at the same time recharging the capacitor for the next cycle. The charge is then amplified via external charge-sensitive amplifiers 6 and digitized. Bias lines 7 and contact pads 8 are also typically provided.
Flat panel imagers have the potential of replacing conventional detectors and radiographic film in some areas of medical imaging. The spatial resolution that can be achieved with these detectors supersedes that of the other detector technologies. The sizes in which they can be manufactured are larger than those of charge coupled devices (CCD) based detectors. Several researchers have reported attempts to use FPIs for cone beam tomography (CBCT). However, their use in tomography has been hampered by various problems, such as gain and offset fluctuations, image lag, readout noise, slow readout rate, etc. One of the major problems is an inadequate dynamic range of FPIs for most tomographic applications which require a good low contrast resolution.
The dynamic range of these detectors is the ratio between the largest and smallest X-ray exposures that can be detected. The largest detectable signal is defined as the signal that saturates the detector while the smallest signal is the noise level in the signal. As a consequence of the small dynamic range, an image of an object that varies in density considerably will have areas that are either overexposed (saturated) or underexposed (below noise level). One faces an analogous problem in photography—when the photographed scene has both high and low light areas, parts of the photograph will appear either as too bright, or even uniformly white, due to overexposure, or too dark, or even uniformly black due to underexposure.
The dynamic range of flat panel detectors is of the order of magnitude of 10,000 while the range that is needed for CT is of the order of 1,000,000. It follows that in order to make a tomographic device based on these detectors, a method for improving their dynamic range needs to be developed.
Similar Technologies
There are a number of existing approaches for capturing a high dynamic range image with a low dynamic range image detector. While some of the methods originate from the field of X-ray imaging, most proposed methods originate from other fields including optical imaging (photography and video), ultrasound imaging, infrared imaging, and digitizing scanners. Overviews are given in references [2] and [3]. Also, by modifying slightly Nayar's classification [2], one may classify the existing methods into five groups: sequential exposure change, multiple image detectors, multiple sensor elements within a pixel, alternative pixel designs, and spatially varying pixel exposures. Each group will be briefly described and representative methods referenced.
Sequential Exposure Change
In this approach, the scene is usually pictured twice, one time with high exposure, the other time with low exposure. The two images are then combined through a mathematical algorithm. The resulting image effectively has an increased dynamic range over the two images it was made from.
This approach has been applied by Viceconti [4] to expand the dynamic range of film in X-ray densitometric analysis of total hip replacement. The final image is generated calculating for each pixel the value: LDE=LLE+LHE, where LDE is the gray level value in the final image, LLE is the gray level exposure in the low exposure image and LHE is the gray level exposure in the high exposure image.
A similar approach has been used by Madden [5] for CCD cameras in optical imaging. Instead of combining the images, Madden, however, takes into account only the highest exposure for a given pixel at which the pixel does not saturate. He justifies this by pointing out that pixels observed at higher exposure times have less quantization noise than do pixels taken at lower exposure. This is effectively a “cut and paste” approach since the parts of the high exposure image which do not saturate are pasted into the final image and the complementary parts are pasted from the low exposure image.
Robertson et al. [3, 6] have improved Madden's approach by eliminating what they perceived as two limitations of his approach. First, in Madden's approach, the camera response is assumed to be linear, which it usually is not (especially in case of consumer video cameras), and the authors propose a method for estimating the camera response. Second, as mentioned above in this “cut and paste” method, only one pixel from the multiple images is used in the final image, and the authors propose that the final image be obtained as a weighted sum of the multiple images. The weights are chosen based on our confidence that the observed data is accurate. For CCD cameras, the data are most accurate if it falls in the middle of the detector range, for example for 8 bit images, which are in the range of 0-255, the most confidence will be for the values around 128 and the least confidence for values around 0 and around 255. Consequently, the authors weight the data with a Gaussian function around 128 and use a maximum likelihood approach to estimate the values.
Similar approaches to Robertson's had been previously proposed in [7] and [8]. Methods and devices for extending the dynamic range of electronic imaging systems based on multiple exposures are also the subject of patents [9], [10] and [11].
Another method is proposed by Alston [12], in which the image of the scene is recorded during a first exposure interval in which the scene light is not artificially illuminated and a second exposure interval illuminated by a flash of artificial illumination. The two images are then combined into a wider dynamic range image.
Seppi et al. [13] describe a dual exposure method for extending the dynamic range of an image intensifier tube (IIT) based X-ray CT detector. This method is also part of patents [14, 15] [16]. The charge accumulated in the photodiodes is sampled using a long and a short sampling interval. When the magnitude of the visible light output from the IIT exceeds a threshold value, the measurement from the short sampling interval is used. Conversely, when the magnitude is less than the threshold value, the measurement from the long sampling interval is used. When the measurement from the short interval is used, it is multiplied by a scaling factor. This scaling factor is determined with a calibration light source by comparing the long interval measurement to the short interval measurement. The scaling factor is then adjusted to obtain a best least squares fit between the long interval measurement and the scaled short interval measurement. The threshold value for use of the short interval measurement is selected to be at a point close to, but less than, the saturation level for the photodiodes. In practice, the transition between use of the short interval measurement and the long interval measurement is made by using a weighted combination of the long interval sample and the short interval. In one embodiment of the invention, a transition range of sample magnitudes is defined, and for sample magnitudes below the transition range, the long interval measurement is used. For sample magnitudes above the transition range, the scaled short interval measurement is used. Finally, for sample magnitudes within the transition range, the weighted combination is used. In order to maintain X-ray photon statistics on a 16″ (40 cm) diameter body, a detector with a minimum signal-to-noise ratio (S/N) of at least 200,000:1 is necessary. This is assuming a typical surface dose of 2 rads/scan and no compensating bolus around the patient. It is also necessary that the IIT, lens optics and photodetector yield an X-ray to electron quantum efficiency of greater than unity.
Multiple Image Detectors
Primarily used in optical imaging, this method utilizes two detectors with different sensitivities that take a picture of the scene at the same time (by using beam splitters, for example). The two images are then combined through a mathematical algorithm into one image with effectively an increased dynamic range. An example is the method by Ikeda [17].
As noted by Nayar [2], a major disadvantage of these methods is their cost due to the need for multiple detectors, precision alignment optics and additional image capture hardware.
Multiple Sensor Elements Within a Pixel
An example of a method that utilizes multiple sensor elements within a pixel is the one proposed by Street [18]. The method is intended to improve the dynamic range of a-Si:H flat panel imaging arrays. The sensor area of each pixel is segmented into a plurality of discrete portions. Each discrete portion has a distinctive responsiveness to incident illumination, either with regard to capacitance or sensitivity. The portions are in common communication with a data line where the charges are being summed. Since the dynamic range of the cumulative discrete portions is greater than any single one sensor portion, a wider dynamic range for sensor operability is achieved. An additional benefit according to the author is a nonlinear response to the incident light which, as such, mimics the human eye and further improves the dynamic range. Paper by Nayar [2] references similar solutions for CCD cameras.
Alternative Pixel Designs
References [19] and [20] propose detector designs where each pixel on the device includes a computational element that measures the time it takes to attain full potential well capacity. Since the full-well capacity is the same for all pixels, the time to achieve it is proportional to image irradiance. The recorded time values are read out and converted to a high dynamic range image. Wen [21] describes a high dynamic range CCD design. The high dynamic range is achieved by providing the charge-coupled device with a nonlinear relationship between the charge accumulated in the photosite and the charge which is transferred out to surrounding circuitry from the photosite.
Merrill [22] proposes a design of a CMOS active cell design with self-reset. Each time the signal from a cell reaches the preset threshold value, the cell is reset and the number of resets is stored for each cell. By resetting the cell a plurality of times, the dynamic range is increased.
Azim [23] describes an imager (CCD, CMOS, . . . ) which utilizes a per-pixel automatic gain control as a means for achieving a higher dynamic range.
Handy [24] proposes a hardware modification of a CCD camera in which the photoelectric charge is injected directly from the photosite area to the output shift register.
Spatially Varying Pixel Exposures
In approaches from this group, several neighboring pixels are clustered and each pixel in the cluster has different sensitivity. The signals from the pixels in the cluster are then combined through a mathematical algorithm into one image with effectively an increased dynamic range. Different sensitivities can be achieved in various ways including: placing a mask with cells of different optical transparencies in front of the array (in optical imaging), etching the pattern on the detector, or using different integration times for different pixels. Nayar [2] suggested an approach that extends the dynamic range of a CCD camera in this manner.
Kan [25] proposes a modification of a CCD camera in which the dynamic range is extended through control of the light intensity level in an image on a pixel-by-pixel basis. In one aspect, a multi-faceted light controller is used like a variable iris camera, but on a pixel-by-pixel basis.
Gaalema [26] proposes a method for extending the dynamic range of infrared image sensing arrays. Infrared sensing arrays are two dimensional arrays of pixels. While scanning, they generally move in the direction normal to the orientation of their columns. The columns are time-delayed and averaged so that effectively only one column is scanned.
Patent [27] describes an extended dynamic range image digitizing apparatus used to digitize image-containing-media into electronic form. The dynamic range is extended by either controlling the illumination of the image surface or the time that the detector is sensitive to the light, or both.
Other Relevant References
Relevant, but so far not referenced documents are: [28], [29] and [30]. A pixel binning method in X-ray detectors including a-Si:H flat panel detectors is a subject of [31].
All bracketed numbered references referred to herein are listed in Appendix A.
The U.S. Pat. No. 5,978,518, to Oliyide et al., provides for image enhancement in digital image processing. Disclosed is a process to enhance a digital image, focusing on enhancement of medical radiographic images. Initially, an image is decomposed into low and high frequency images. A dynamic range modification is performed on the low frequency image and the high frequency image is modified through noise estimation, anatomical regions of importance, and edge estimation. These separate images are then combined together to form a finalized image. Additionally, pixel values of the finalized image may be shifted to map the values into a desired range.
The U.S. Pat. No. 6,067,342, to Gordon, provides for a digital filmless X-ray projection imaging system and method. Disclosed is a system and method for producing digital X-ray projection images. The process of obtaining the image includes a plurality of detectors, which receive the X-ray beams, and are rotated about the transmission axis to generate a set of projection data at a plurality of angular positions. The detector array can be arranged in two rows offset from each other to effectively provide higher quality image data. Once a complete set of projection data is obtained, a reconstruction computer processes the data set combining projection data at the different angles to produce the desired range.
The U.S. Pat. No. 6,263,040 B1, to Hsieh, provides methods and apparatus for cone-tilted parallel sampling and reconstruction. Disclosed is a method for generating an image using a digital flat panel detector. The digital X-ray panel includes a plurality of detector cells and uses a predefined triggering sequence to activate a signal activation line. This sequence provides a set of parallel and tilted parallel samples where projections are then collected relating to the tilted parallel beam geometry. With these projections, an image is then generated using a specific reconstruction algorithm.
The U.S. Pat. No. 6,272,207 B1, to Tang, provides a method and apparatus for obtaining high-resolution digital X-ray and gamma ray images. Disclosed is a device that produces high-resolution X-ray and gamma ray images. The device consists of a radiation mask composed of both an opaque portion and a plurality of apertures. As the radiation mask moves across the object, the apertures allow selected portions to be imaged by the detector effectively decreasing charge smear due to an X-ray incidence angle close to zero. The steps of moving the detector pixels and mask are repeated until every portion of the object is imaged. This digital data is then combined to produce a high resolution image of the object.
The following U.S. patents are also related to the present invention: U.S. Pat. Nos. 5,717,223; 6,009,197; 6,041,097; 6,069,361; 6,232,606 B1; 6,233,308 B1; 6,256,370 B1; and 6,324,241 B1.